Devices for monitoring physiological information such as heart rate, and other exercise related information such as speed and distance exist. Such devices provide a means of representing the quality of the exercise conducted by a user wearing the device or exercising on a machine incorporating the device. It is often difficult for the user to effectively interpret this representation of quality or use it to compare it against their goals. The state of the art has therefore developed further to provide systems compatible with a particular measurement device for downloading and uploading exercise data to and from the device for processing, with the object of making the data more meaningful for the user. For example, systems are available which enable a user to upload a workout program onto a wearable exercise device. The user exercises according to the program while the device monitors the user's heart rate. Once the exercise is complete, the user downloads the monitored data to a computer to process the data in some cases against the workout program and provide statistics on that particular exercise session.
In the case of exercise, in at least some cases automated exercise devices are rigid in their approach to a user exercising. For example, a preloaded training workout may stipulate a 2 minute warm up followed by a 4 minute interval at a speed close to half marathon race pace followed by easy recovery running for 3 minutes before another 4 minute half marathon race pace effort. There are two fundamental problems with this. Firstly the user may reach a hill just before he/she starts the 4 minute half marathon race pace effort and have to run it up the hill. The user may then end up having to do the second 4 minute half marathon race pace effort just as they reach the crest of the hill and descend. This rigidity of set training for each activity within the workout or activity can mean that the user experience is poor and that the data obtained will have far greater variability and error within it, potentially making it almost meaningless to analyse and to draw conclusions from. Secondly without understanding the full context of the workout it is difficult for a trainer for example to comment effectively, without for example knowing the terrain or the resistance that the user is experiencing and this can mean interpretation may be reduced to guesswork.
Current systems can also be heavily dependent on the type of monitoring device used as they are generally limited to monitoring specific parameters e.g. heart rate for interpretation of the exercise. Furthermore, interpretation of exercise data in at least some current systems is dependent on pre-established assumptions of the user's exercise regime. In other words, the exercise data is processed with the assumption that the user is performing a specific type of exercise. These factors limit the diversity of the systems and in some cases their accuracy, should the user choose to divert from the type of exercise specified by a particular program. Lastly, the statistics provided by the system may still be meaningless to an unskilled user and would generally require the aid of a specialised trainer to interpret them and provide advice/guidance as to how to modify the user's exercise program so the user can meet their goals.
Exercise and activity devices that measure biometric and environmental data such as heart rate, speed, leg or arm turnover or stroke rate, altitude, temperature, R-R, power, slope, distance per turnover, location, distance and time currently exist. This data is displayed on a watch or device screen or spoken through headphones. These systems are merely measurement devices. This means that once the activity data is collected, the user must have the relevant level of skill to analyze and interpret it and then decide what changes they should make to their future exercise to optimize their time and effort during training and to maximize improvements.
FIG. 3 for example shows data downloaded from a measurement device. It is very difficult to extract any clear understanding or information from such raw data. Hundreds of millions of people around the world exercise ineffectively due to poor understanding of the appropriate strategies to maximizing fitness, sports performance and health improvements through activity. In most cases they do not have access to a coach.
In most cases, users ultimately do not want data from a measuring device, they want to know what was correct about what they did, what problems and solutions they need to work on and what to do next. They need someone or something to interpret the data and provide intelligent feedback.
When tracking a soldier in the field or a user engaging in health related activities throughout a full day, it has been difficult in the past to clearly establish different activities that have occurred during the day, and/or assess levels of fatigue or areas of weakness.
It is an object of the present invention to provide a method and system for classifying exercise and/or activity related data into different activities to provide an accurate representation of a particular exercise session or form of activity, or to at least provide the public with a useful choice.